Trajectory Prediction Training for Adversarially Robust Autonomous Machines
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Solution Overview
Problem
Current trajectory prediction methods for autonomous vehicles are vulnerable to adversarial attacks, which can compromise the accuracy of predicted trajectories and lead to safety issues due to the obfuscation of gradients in probabilistic models and destabilization of adversarial training processes.
Innovation Solution
The implementation of a deterministic approach within probabilistic models to generate adversarial samples, combined with a hybrid objective that interleaves adversarial training with learning from clean data and domain-specific data augmentation, to stabilize the training process and maintain performance on clean data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If probabilistic generative models are used to cope with uncertainty in motion forecasting, then the model can handle uncertain trajectories, but the stochastic components obfuscate the gradients that guide adversarial generation
Solution Approach 1:
The model segments the probabilistic generative process into distinct components: a deterministic trajectory predictor that provides clear gradient paths, and a separate uncertainty modeling module that handles stochasticity. This segmentation allows gradients to flow clearly through the deterministic part while uncertainty is modeled separately, resolving the contradiction between handling uncertainty and maintaining gradient guidance for adversarial robustness.
Solution Approach 2:
The patent introduces an intermediary deterministic component that mediates between the stochastic uncertainty model and the adversarial training process. This deterministic intermediary provides a clear gradient path for adversarial generation while the uncertainty model operates as a separate layer, allowing both uncertainty handling and reliable gradient guidance to coexist.
2Reliability
If adversarial training aims to model joint data distribution of future trajectories and adversarial past trajectories, then the model can learn from adversarial examples, but the co-evolution of adversarial sample distribution and prediction model destabilizes the training process
Solution Approach 1:
The patent applies preliminary action by first training the model on clean data to establish a stable baseline prediction model, then gradually introducing adversarial examples in a controlled manner. This staged approach prevents the co-evolution instability by establishing a stable foundation before exposing the model to adversarial perturbations, allowing the model to adapt progressively without destabilization.
Solution Approach 2:
The training process uses periodic alternation between clean data training and adversarial data training. The model periodically returns to clean data to stabilize its predictions, then switches to adversarial data to improve robustness. This periodic cycling prevents the destabilizing co-evolution by resetting the training distribution at regular intervals.
3Reliability
If adversarial training is performed extensively, then the model becomes more robust against attacks, but performance on clean data degrades
Solution Approach 1:
The patent applies partial action by performing adversarial training on only a portion of the training data or for a limited portion of training epochs, rather than extensively training on all adversarial examples. This partial adversarial training is sufficient to achieve robustness while leaving the majority of training capacity dedicated to maintaining performance on clean data, thus resolving the contradiction between robustness and clean data performance.
Solution Approach 2:
The training process dynamically changes parameters such as the adversarial training ratio, perturbation strength, and loss function weights during training. By adjusting these parameters, the model can achieve adequate adversarial robustness while preserving clean data performance, avoiding the degradation that occurs with extensive fixed-parameter adversarial training.
Data Source
AI summary
In various examples, robust trajectory predictions against adversarial attacks in autonomous machines and applications are described herein. Systems and methods are disclosed that perform adversarial training for trajectory predictions determined using a neural network(s). In order to improve the training, the systems and methods may devise a deterministic attach that creates a deterministic gradient path within a probabilistic model to generate adversarial samples for training. Additionally, the systems and methods may introduce a hybrid objective that interleaves the adversarial training and learning from clean data to anchor the output from the neural network(s) on stable, clean data distribution. Furthermore, the systems and methods may use a domain-specific data augmentation technique that generates diverse, realistic, and dynamically-feasible samples for additional training of the neural network(s).